Chapter 1 : Artificial Intelligence in Theoretical Chemistry: Redefining Molecular Frameworks through Quantum Simulation and Wavefunction Prediction

Background: Recent advances in artificial intelligence have begun to fundamentally reshape theoretical and computational chemistry by addressing long-standing limitations of traditional quantum-mechanical methods. In particular, the growing complexity of molecular systems has driven the need for approaches that combine physical theory with data-driven intelligence. This chapter situates AI as a transformative framework within modern theoretical chemistry. Objective: This chapter aims to examine how artificial intelligence redefines molecular frameworks through quantum simulation and wavefunction prediction. Method: A conceptual and analytical approach is adopted, integrating quantum-mechanical foundations with modern machine learning architectures. Established electronic structure methods are discussed alongside AI-driven wavefunction models and data-intensive simulation techniques. Key findings: The chapter demonstrates that AI-enhanced models can surpass traditional methods in accuracy and scalability for complex quantum systems. It highlights the effectiveness of deep learning–based wavefunction representations and AI-assisted Hamiltonian modeling. Key challenges related to interpretability, generalization, and data limitations are also critically analyzed. Implications: The findings indicate a paradigm shift toward AI-assisted and potentially autonomous theoretical chemistry, with significant implications for molecular design, materials discovery, and future quantum technologies. How to cite: Kodiyatar, N. (2025). Artificial Intelligence in Theoretical Chemistry: Redefining Molecular Frameworks through Quantum Simulation and Wavefunction Prediction. Zenodo. https://doi.org/10.5281/zenodo.15503464